Evaluation of Sunflower (Helianthus annuus L.), Sorghum (Sorghum bicolor L.) and Chinese Cabbage (Brassica chinensis) for Phytoremediation of Lead Contaminated Soils
Bibliographic record
Abstract
The problems associated with heavy metal contamination are widespread and are especially common in developing countries. A pot study was carried out to evaluate the effectiveness of Sunflower (Helianthus annuus), Sorghum (Sorghum bicolor) and Chinese cabbage (Brassica chinensis) at removing lead from the soil. Lead contaminated soils were collected from Kabwe near the old Lead mine and characterized for total and extractable lead, pH, organic matter, texture and cation exchange capacity. The average total and extractable lead concentrations were 23 313 and 5876 mg/kg, respectively, in contaminated soil compared to 57.75 and 10.02 mg/kg in uncontaminated soil. The contaminated soil was then diluted with uncontaminated soil to achieve five contamination levels of 5876, 2500, 1000, 500 and 10.02 mg/kg. Test plants were grown for 10 weeks after which below and above ground dry biomass yields were determined and tested for lead concentration and uptake. Results from this study show that Chinese cabbage is more effective at lead uptake than Sunflower and Sorghum. Results also show that high soil lead concentration results in poor plant growth, low biomass yield and increased lead accumulation in plant tissue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".